SRI 2017 Weather Over Time

Required Packages

suppressMessages(library(tidyverse))
suppressMessages(library(dplyr))
suppressMessages(library(ggplot2))
suppressMessages(library(ggmap))
suppressMessages(library(EBImage))
suppressMessages(library(knitr))
suppressMessages(library(rmarkdown))
 #suppressMessages(library(xlsx)) #Used for Data Cleaning
 #suppressMessages(library(magrittr)) #Used for Data Cleaning

Read in Data

All data sourced from Saint Thomas University Earth Networks weather station located at Biscayne College.

WL1 <- read.csv("/Users/KadenLoring/Documents/SRI_17/Spreadsheets/SRI_Official_CSV.csv")
#Preliminary Data Frame with some weather station errors present.

Cleaning Data

The weather station data occasionaly records empty (ghost) rows or observations.

WL2 <- na.omit(WL1) #omits ghost rows

The data frame, “WL2”, still contains some erroneous observations recorded by the weather station which are easily characterized by a consistent fahrenheit temperature reading of 32° (conditions clearly not present in Miami, FL). The errors are likely caused by temporary power outages.

Error_32 <- WL2$TempF<40 #vector containing row numbers with temperatures less than 40° F; the lowest true temperature over the observed time frame.

which(Error_32) #displays which levels contain "Error_32"

## [1]    35 10959 11295 12100

#rows containing Error_32: 35, 10959, 11295, 12100

WL3 <- WL2[-c(35, 10959, 11295, 12100),] #erroneous rows removed from data frame

Compare Data Frame Pre and Post Cleaning

Scatter plot of Temperature over the Time Interval for WL1 (uncleaned data) and WL3 (cleaned data). [Notice the outliers that occur at 32° F]

#Uncleaned Plot
Temp_1 <- plot(WL1$TimeInterval, WL1$TempF, type = "p", main="Temperature F°", sub = "Measured in 5 minute Time Intervals March 1 - June 4", xlab = "Time Interval", ylab = "Temperature")

Temp_1

## NULL

#Cleaned Plot
Temp_3 <- plot(WL3$TimeInterval, WL3$TempF, type = "p", main="Temperature F°", sub = "Measured in 5 minute Time Intervals March 1 - June 4", xlab = "Time Interval", ylab = "Temperature")

Temp_3

## NULL

Create Labels for Various Variables

#Line Plots of ALL Variables
TimeInt <- WL3$TimeInterval
Temp <- WL3$TempF
HumidPerc <- WL3$RelHumidityPerc
DewPoint <- WL3$DewPointF
WetBulb <- WL3$WetBulb
WindDir <- WL3$WindDir
WindSpeed <- WL3$WindSpeedmph
WindChill <- WL3$WindChillperHIF
BaroPres <- WL3$BaroPressureinchHg
RainRate <- WL3$RainRateInperHr
RainDay <- WL3$RainDayIn
LightPerc <- WL3$LightPerc
TempRate <- WL3$TempRateFperHr.
HumidRate <- WL3$HumidityRatePercperHr
PresRate <- WL3$PressureRateinHgperHr.
Date <- WL3$Time

Plots

Define graphical functions.

#define time labels for x axis
tendays <- scale_x_continuous(breaks=c(1,2879, 5759,   8639, 11519, 14399, 17279, 20159, 23041, 25923, 27665),
labels=c("3/1", "3/10", "3/20", "3/30", "4/9", "4/19", "4/29", "5/9", "5/19", "5/29", "6/4"))

#define common plot background aesthetic
commonplot <- theme(plot.background = element_rect(fill="snow"),
        panel.background = element_rect(fill="white"))

Various graphical representations of data observations.

#Temperature over Time Intervals smooth plot
Temp_smooth1 <- ggplot(WL3, mapping=aes(TimeInt,Temp)) +geom_smooth(color="firebrick")+geom_point(alpha=0.01,  color="skyblue", size=0.5)+
labs(x="Time Intervals",  y="Temperature °F", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Temperature °F" , subtitle ="March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL" )+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

Temp_smooth1

#Relative Humidity Percentage over Time Intervals smooth plot
HumidPerc_smooth1 <- ggplot(WL3, mapping = aes(TimeInt, HumidPerc)) + geom_point(alpha=0.05, color="skyblue", size=0.5) + geom_smooth(color="blue")+
  labs(x= "Time Intervals", y= "Relative Humidity %", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle ("Relative Humidity Percentage", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL" )+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

HumidPerc_smooth1

#Dew point over Time Int smooth plot
DewPoint_smooth1 <- ggplot(WL3, mapping = aes(TimeInt, DewPoint)) + geom_smooth(color="orange")+
geom_point(alpha=0.01, color="skyblue", size=0.5)+
labs(x= "Time Intervals", y= "Dew Point °F", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Dew Point °F", subtitle = "March 1 - June 4, 2017\n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

DewPoint_smooth1 

#Wet Bulb °F over Time Intervals smooth plot
WetBulb_smooth1 <- ggplot(WL3, mapping = aes(TimeInt, WetBulb)) + geom_smooth (color="deeppink1")+ geom_point(alpha=0.01, color="skyblue", size=0.5)+
labs(x= "Time Intervals", y= "Wet Bulb °F", caption= "(One Time Interval Recorded Every Five Minutes)") + ggtitle("Wet Bulb °F", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  coord_cartesian(ylim = c(40, 90))+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

WetBulb_smooth1

#Wind Speed over Time Int Smooth plot
WindSpeed_smooth1 <- ggplot(WL3, mapping = aes(TimeInt, WindSpeed)) + 
geom_smooth(color= "purple") +geom_point(alpha=0.05,    color="skyblue", size=0.5)+ labs(x="Time Intervals", y= "Miles Per Hour", caption= "(One Time Interval Recorded Every Five Minutes)")+ 
  ggtitle("Wind Speed in Miles Per Hour", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

WindSpeed_smooth1

#Wind Chill °F over Time Intervals smooth plot
WindChill_smooth1 <- ggplot(WL3, mapping = aes(TimeInt,WindChill)) + 
geom_smooth(color= "darkblue") + geom_point(alpha=0.01,  color="skyblue", size=0.5) + labs(x="Time Intervals",  y= "Wind Chill °F", caption= "(One Time Interval Recorded Every Five Minutes)") +
  ggtitle("Wind Chill °F", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

WindChill_smooth1

#Barometric Pressure over Time Intervals smooth plot
BaroPres_smooth1 <- ggplot(WL3, mapping = aes(TimeInt, BaroPres))+ 
  geom_smooth(color="red")+geom_point(alpha=0.01, color="skyblue", size=0.5)+ labs(x="Time Intervals", y= "Inches of Hg", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Barometric Pressure in Inches of Mercury", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

BaroPres_smooth1

#Rain Rate over Time Intervals smooth plot
RainRate_smooth1 <- ggplot(WL3, mapping = aes(TimeInt,RainRate))+
  geom_smooth(color="royalblue")+ geom_smooth(color= "orchid1", method = "lm", se = F) + labs(x= "Time Intervals", y= "Inches Per Hour", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Precipitation Rate in Inches Per Hour", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

RainRate_smooth1

#Light % over Time Intervals smooth plot (with scatter plot background)
LightPerc_smooth1 <- ggplot(WL3, mapping = aes(TimeInt, LightPerc))+ 
  geom_smooth(color="gold", method="lm", se=F)+ geom_point(alpha=0.1, color="skyblue", size=0.3)+ geom_smooth(color="sienna1")+labs(x= "Time Intervals", y= "Light %", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Light Percentage", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) +
coord_cartesian(ylim = c(0,35)) + tendays + commonplot

LightPerc_smooth1

#Light % over Time Intervals smooth plot (clearly visible increase)
LightPerc_smooth2 <- ggplot(WL3, mapping = aes(TimeInt, LightPerc))+ 
  geom_smooth(color="gold", method="lm", se=F)+ geom_smooth(color="sienna1")+labs(x= "Time Intervals", y= "Light %", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Light Percentage", subtitle = "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL")+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8)) + tendays + commonplot

LightPerc_smooth2

Campus Visualization

In order to aid comprehension of wind direction, it is important to provide map images of Saint Thomas University campus. The package ggmap is used extensively for map images.

#Google Hybrid Map centered on whole campus
STU_Hyb <- get_map(location= c(lon= -80.25555, lat= 25.92325), zoom = 17, maptype = "hybrid")

STU_Hyb1 <- ggmap(STU_Hyb, extent = "device")+ geom_point(data = NULL, aes(x = -80.25375, y = 25.92312),  
   lwd = 3, colour = "red") + geom_text(aes(label= "BC Weather Station"), color= "red", x=-80.2540, y=25.9236, size= 4)+
  ggtitle("Biscayne College Weather Station", subtitle = "Saint Thomas University, Miami, FL") + 
  theme(plot.title = element_text(color= "red")) 

suppressMessages(STU_Hyb1)

#Google Hybrid Map centered on Biscayne College
BC_Hyb <- get_map(location= c(lon= -80.25405, lat= 25.92325), zoom = 17, maptype = "hybrid")

BC_Hyb1 <- ggmap(BC_Hyb, extent = "device")+ geom_point(data = NULL, aes(x = -80.25375, y = 25.92312),  
   lwd = 3, colour = "red") + geom_text(aes(label= "BC Weather Station"), color= "red", x=-80.2540, y=25.9236, size= 4) +   ggtitle("Biscayne College Weather Station", subtitle = "Saint Thomas University, Miami, FL") + theme(plot.title = element_text(color= "red")) 

BC_Hyb1

Clean WL3 to Create a Wind Rose

Wind roses are used to visualize wind direction. WL3 contains some blank observations for the wind direction variable.

#Clean WL3 of blank observations in wind direction variavble 
Rose <- subset(WL3, WindDir != "") #remove blank records

Rose <- factor(Rose$WindDir) #save changes made by subset

Rose <- factor(Rose, levels = c("N","NNE", "NE", "ENE", "E", "ESE", "SE", "SSE", "S", "SSW", "SW", "WSW", "W", "WNW", "NW", "NNW")) #reorder by direction instead of alphabetically

Create Wind Rose

#Standard Wind Rose
Rose1 <- ggplot(data.frame(Rose), aes(x=Rose, fill=Rose))+
  geom_bar(stat="count",width=1,colour="black",size=0.1, alpha= 0.5)+
  coord_polar(theta = "x", start=6.0729, direction = 1)+
  scale_color_discrete ()+ ggtitle("Frequency Wind Rose", subtitle= "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL") + 
  xlab("Wind Direction Recorded Every Five Minutes")+ylab("")+ guides(fill=guide_legend(title="Direction")) + 
  theme(plot.title = element_text(size=18, face= "bold", color = "red"), axis.title.x = element_text(size= 7), axis.text.y=element_blank(), axis.ticks.y = element_blank()) 

Rose1

Create Wind Rose to be Used on Google Map Image

To ensure aesthetic quality make text color lighter, background and plot transparent, remove gridlines, and remove legend.

WindRose2 <- ggplot(data.frame(Rose), aes(x=Rose, fill=Rose))+
  geom_bar(stat="count",width=1,colour="black",size=0.1, alpha= 0.5)+
  coord_polar(theta = "x", start=6.075, direction = 1)+
  scale_color_discrete ()+ ggtitle("Frequency Wind Rose")+
  xlab("Wind Direction Recorded Every Five Minutes")+ ylab("")+ guides(fill=guide_legend(title="Direction")) + theme(plot.title = element_text(size=18, face= "bold", color = "burlywood1", vjust = -3, hjust = -0.15), axis.title.x = element_text(size= 8, color = "cornsilk1", hjust = 1.05, vjust = 1.4), 
        axis.text.y=element_blank(), axis.ticks.y = element_blank(), axis.text.x = element_text(colour = "cornsilk2", size= 12, face = "bold"),
        legend.position = "none")+
  theme(panel.grid.major = element_line(colour = NA), axis.line = element_line(colour = NA), plot.background = element_rect(fill= "transparent", colour= NA), panel.background = element_rect(fill= "transparent", colour = NA))

WindRose2

Wind Rose Map

The EBImage package allows for the images of WindRose2 and a Google Map to be transposed over each other.

#Map Centered on Biscayne College
BC <- get_map(location= c(lon= -80.25405, lat= 25.92325), zoom = 17, maptype = "hybrid")

Map_BC <- ggmap(BC, extent = "device")

Map_BC

#Map Centered on STU Campus
STU <- get_map(location = c(lon=-80.25555, lat= 25.92325 ), zoom = 17, maptype = "hybrid")

Map_STU <- ggmap(STU, extent = "device")

Map_STU

Save WindRose2, Map_BC, and Map_STU as png Files

EBImage needs image files, such as png, in order to manipulate images.

ggsave(WindRose2, file="WindRose2.png", bg= "transparent", device = NULL )

ggsave(Map_BC, file= "Map_BC.png", device = NULL)

ggsave(Map_STU, file= "Map_STU.png", device = NULL)

Read Images Back to R

Use readImage function to read the images into R.

WindRose2_png <- readImage("/Users/KadenLoring/Documents/SRI_17/rscriptsSRI/Pub/WindRose2.png")

Map_BC_png <- readImage("/Users/KadenLoring/Documents/SRI_17/rscriptsSRI/Pub/Map_BC.png")

Map_STU_png <- readImage("/Users/KadenLoring/Documents/SRI_17/rscriptsSRI/Pub/Map_STU.png")

Transpose Wind Rose Image Onto Map Image

#Wind Rose Map centered on Biscayne College
WindRoseMap1 <- (WindRose2_png + Map_BC_png)

display(WindRoseMap1, method = "raster")

#Wind Rose Map centered on STU campus
WindRoseMap2 <- (WindRose2_png + Map_STU_png)

display(WindRoseMap2, method = "raster")

Wind Direction Frequency Bar plot

Additionally, to aid in understanding wind direction frequency, I have added a frequency bar plot.

#Barplot of Wind Directions 
WindDir_Barplot <- ggplot(data.frame(Rose), aes(x=Rose, fill= Rose))+ geom_bar()+
  ggtitle("Wind Direction Frequency Barplot", subtitle= "March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL" )+
  labs(x="Wind Direction", y="Frequency", caption= "Direction Recorded Every Five Minutes")+theme(plot.caption = element_text(size = 8))+
  theme(plot.title = element_text(color = "red", face = "bold"), legend.title = element_text(size = 8), legend.text = element_text(size=7)) + guides(fill=guide_legend(title="Direction"))+
  scale_color_discrete() + 
  theme(plot.title = element_text(color="Red", face = "bold"), 
  plot.caption = element_text(size= 8), axis.text.x= element_text(size=5)) + commonplot

WindDir_Barplot

Measures of Air Humidity

The measurements of air temperature (dry bulb temperature), wet bulb temperature, and dew point temperature are all related.

Dry bulb temperature is a measure of ambient air temperature without effect of air moisture taken into account.

Wet bulb temperature is a measure of adiabatic saturation temperature. Wet bulb takes into account the cooling effect of evaporation on temperature. Wet bulb is always between dry bulb and dew point. [When wet bulb equals dry bulb temperature, a system is said to be in 100% humidity, or saturation].

Dew point temperature is a measure of the temperature at which water vapor begins to condense out of the air. If dew point is close to dry bulb temperature, the relative humidity is high; and vice versa. Dew point temperature is always less than or equal to dry bulb temperature.

#Wet Bulb should always be between ambient temperature (dry bulb) and Dew point

HumidityMeasures <- ggplot()+
  geom_smooth(data = WL3, mapping = aes(TimeInt, Temp, color = "firebrick"))+
  geom_smooth(data = WL3, mapping = aes(TimeInt, WetBulb, color= "royalblue"))+
  geom_smooth(data = WL3, mapping = aes(TimeInt, DewPoint, color= "green"))+
  labs(x="Time Intervals",  y="Degrees Fahrenheit", caption= "(One Time Interval Recorded Every Five Minutes)")+
  ggtitle("Measures of Humidity" , subtitle ="March 1 - June 4, 2017 \n Saint Thomas University, Miami, FL" )+
  theme(plot.title = element_text(color="Red", face = "bold"), plot.caption = element_text(size= 8), legend.title = element_text(color = "red"))+
  guides(color=guide_legend(title="Measurements"))+
  scale_color_discrete(labels= c("Dry Bulb Temperature", "Dew Point Temperature", "Wet Bulb Temperature")) + commonplot
  
HumidityMeasures